Package index • CrossAncestryGenPhen

Main DGE functions

limma_interaction_effect(): Limma-Based Interaction Test

perm_interaction_effect(): Simple Permutation-Based Interaction Test (No Bootstrapping, No Convergence)

subset_limma_interaction_effect(): Subset Limma Interaction Effect with Stratified Cross-Validation

subset_perm_interaction_effect(): Parallel Subset-Based Interaction Effect Analysis

Simulation functions

estimate_params(): Estimate RNA-seq Model Parameters from Count Data

sim_2group_expression(): Simulate RNA-seq Expression Data for Two Groups

sim_4group_expression(): Simulate RNA-seq Expression Data for Four Groups (Two Ancestries × Two Conditions)

sim_imbalanced_ancestry(): Simulate imbalanced ancestry sampling across two cohorts

Visualization Functions

plot_BCV(): Mean–Dispersion Scatter Plot with Optional Overlay

plot_confusion_matrix(): Plot a binary confusion matrix as a heatmap with TP, FP, FN, TN labels.

plot_correlation_difference(): Plot correlation differences with optional facets

plot_correlation_heatmap(): ComplexHeatmap of Correlation Matrix Across Iterations

plot_estimated_dispersions(): Plot Estimated Gene Dispersions Between Two Datasets

plot_estimated_means(): Plot Estimated Gene Means Between Two Datasets

plot_expression_heatmap(): Expression Heatmap with Ancestry and Group Split

plot_feature(): Plot feature distributions across two splits

plot_imbalanced_groups(): Plot Imbalanced Groups

plot_jaccard_heatmap(): Plot Jaccard Heatmap of Sample Reuse Across Iterations

plot_mean_variance_density(): Plot Mean-Variance Relationship with Density Overlay

plot_mean_variance_trend(): Plot Mean-Variance Trend Using log2-CPM (voom-style)

plot_null_distribution(): Plot permutation-based T-statistics with observed values and p-values

plot_pca_cluster(): PCA Cluster Plot

plot_pvalue_concordance(): Plot concordance of -log10 p-values between two methods

plot_pvalue_distribution(): Plot P-value Distribution Colored by a Binned Fill Variable

plot_qq_correlation(): Faceted QQ plots for specified genes

plot_sensitivity_specificity(): Plot sensitivity and specificity as a bar plot.

plot_sim_interaction_effect(): Plot Simulated Interaction Effects

plot_sim_main_effect(): Plot Simulated Main Effects

plot_stratified_feature(): Plot stratified feature distributions across splits

plot_stratified_sets(): Plot stratified ancestry sets.

plot_tsne_cluster(): t-SNE Cluster Plot

plot_volcano(): Volcano Plot

Utility Functions

split_stratified_ancestry_sets(): Split Expression and Metadata into Reference (R), Subset (X), and Inference (Y) Sets

track_sample_ids(): Track Sample Roles and IDs from a Split

compute_jaccard_matrix(): Compute Jaccard Similarity Matrix Between Iterations

compute_correlation_matrix(): Compute Correlation Matrix from Long-format Data

compute_qq_correlation(): Gene-wise quantile correlations

summarize_subsets(): Summarize subsets results by feature, with Cauchy-combined p-value

ggsaveDK(): Save ggplot with optional legend removal and sensible defaults

Plot themes

theme_nature_fonts(): Nature-Inspired Font Sizes Theme (Internal)

theme_small_legend(): Small Legend Theme (Internal)

theme_white_background(): A clean white ggplot2 theme with optional facet labels

TESTING

boot_interaction_effect(): Simple Bootstrap-Based Interaction Estimation with CI Level Control

boot_correlation_diff(): Bootstrap-Based Correlation Difference Test (Unstratified)

loo_interaction_effect(): Leave-One-Out Diagnostic for interaction effects

perm_prediction_difference(): Permutation Test for Prediction Performance Differences

perm_correlation_difference(): Simple Permutation Test for Correlation Differences